The derivation of a reliable velocity model can be performed through different approaches including model-driven and data-driven methods.
The so-called model-driven methods transform a geological section directly into a velocity model to be used for Pre-Stack Depth Migration (PSDM).
The convergence of the initial velocity estimate to the final velocity model is obtained in a trial-and-error approach consisting of manually changing the distribution of velocity in the model, performing a new PSDM and controlling the post-migration image gathers together with the geologic reliability.
These methods not always are able to provide seismic velocity models that agree with the measured geophysical data (i.e. arrival times of seismic waves, observed gravity anomalies or calculated resitivity functions from electromagnetic measurements) and explore only a limited sub-group of models (some of those which are geologically meaningful for the interpreter).
The so-called data-driven methods, following a more rigorous approach (minimization of a cost function), yield always the fit of the measured data but the final velocity structure might not agree with geological considerations.
Systematic and random errors in the input inversion data, non-uniqueness of the solution and sensitivity of the data to the model parameters (e.g. first-break tomography is more sensitive to high-velocity zones than to low-velocity ones, electromagnetic methods are more sensitive to conductive zones than to resistive ones), provide in many cases a difficult solution of the problem.
The integration of different source of information (geophysical data including seismic and non-seismic, a-priori information and interpretational constraints) reduces the non uniqueness of the solution and provides improved seismic resolution in complex geology conditions.
Data integration approaches were performed by several authors in the past by deriving a model in one of the domains (generally seismic), by transforming the data via empirical functions in another geophysical domain (e.g. density or resistivity) and by performing modeling or inversions in the corresponding non-seismic domain.
In some cases, the so-obtained models could be transformed back into the seismic velocity domain to be used to improve the seismic imaging results.
This approach, whilst valuable in principle, shows several problems in the actual implementation.
One of the most obvious problems consists of the definition of reliable functions relating seismic velocity to density or resistivity to allow the transformation of parameters in different geophysical domains.
The other problem is whilst, from one point of view, the target is the integration of data, the actual implementation of the described workflow provides a larger weight to the seismic-derived model rather than to the other non-seismic methods.
The non-seismic methods, in this approach, are confined to work around an initial model provided by seismic with little chances of substantially modifying it (especially in a linearized inversion approach).